Another widely used fair representation learning paradigm is adding fairness constraints to representations to regularize the model [1]}, [2]}, [3]}.
For example, Zemel et al. zemel2013learning proposed to use autoencoder to learn hidden representations of input data.
They added a statistical parity regularization to reduce the discrimination of hidden representations on sensitive attributes.
Yao et al. yao2017beyond derived four different fairness metrics from the predicted and real ratings of users with different sensitive attributes, and they compared regularizing the collaborative filtering models with one of these metrics.
These regularization based methods can control the tradeoff between accuracy and fairness by choosing different regularization intensities.
However, the fairness constraints are often difficult to achieve and may even contradict the objectives of the main prediction task, and thus the model optimization may not be effective.
In addition, in many real-world applications such as click-through rate prediction and news recommendation, it is difficult to design proper fairness constraints for model training [4]}.
